site quality curves for birch stands in north-western spain

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Silva Fennica 40(4) research articleswww.metla.fi/silvafennica · ISSN 0037-5330

The Finnish Society of Forest Science · The Finnish Forest Research Institute

1 Introduction

The Betula genus is distributed throughout most of Europe, where it is mainly represented by two stand-forming tree species, Betula pubescens Ehrh. and Betula pendula Roth. In the Iberian

Site Quality Curves for Birch Stands in North-Western Spain

Ulises Diéguez-Aranda, José Antonio Grandas-Arias, Juan Gabriel Álvarez-González and Klaus von Gadow

Diéguez-Aranda, U., Grandas-Arias, J.A., Álvarez-González, J.G. & Gadow, K.v. 2006. Site quality curves for birch stands in north-western Spain. Silva Fennica 40(4): 631–644.

A model for predicting the height growth of even-aged, birch (Betula pubescens Ehrh.) dominated stands in Galicia (north-western Spain) was developed. Data from stem analysis of 214 trees were used for model construction. Two dynamic site equations derived with the generalized algebraic difference approach (GADA) were tested, which combine compatible site index and height models in one common equation. Both equations are base-age invari-ant and directly estimate height and site index from any height and age. The fittings were done in one stage using the base-age-invariant dummy variables method. A second-order continuous-time autoregressive error structure was used to correct the inherent autocorrela-tion of the longitudinal data used in this study. Cieszewski’s model best described the data. This model is therefore recommended for height growth prediction and site classification of birch stands in Galicia.

Keywords site quality, generalized algebraic difference approach, base-age invariant dynamic equation, Hossfeld model, Betula pubescens, GaliciaAddresses Departamento de Ingeniería Agroforestal, Universidad de Santiago de Compostela. Escuela Politécnica Superior, Campus universitario, 27002 Lugo, Spain; Gadow: Institut fur Waldinventur und Waldwachstum, George-Auguts-Universität Göttingen. Büsgenweg 5, D-37077 Göttingen, GermanyE-mail [email protected] 17 January 2006 Revised 24 May 2006 Accepted 6 July 2006Available at http://www.metla.fi/silvafennica/full/sf40/sf404631.pdf

Peninsula, these two taxa are freely hybridized, and their taxonomy is somewhat confused, with each possessing a number of different nomen-clatural identities (Castroviejo et al. 1990). Of the two taxa, Betula pubescens is the more oceanic, westerly distributed taxon while Betula pendula

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Silva Fennica 40(4), 2006 research articles

is rarer and occupies more easterly and southerly, high altitude, mesic locations (Stevenson 2000). In Galicia, in north-western Spain, Betula pubes-cens Ehrh. (also referred to as Betula alba L. and Betula pubescens subsp. celtiberica Rothm. & Vasc. – Castroviejo et al. 1990) grows from 0–1700 m above see level, although it is more abundant in the north-eastern mountains of this region at altitudes above 400–500 m. Betula pubescens requires high moisture all year round. Therefore, in the south of Galicia, where the sum-mers are dry, it appears naturally only in areas near the streams or in wet locations. This species may be considered a fast-growing pioneer tree, which readily colonises open ground originated by human activity (burning, cutting or grazing) or natural disturbances. In Galicia there are currently 32 000 hectares of stands that include Betula pubescens as the main tree species (Xunta de Galicia 2001). The management of broadleaf spe-cies, including birch, is important for preserving biodiversity. Although birch is a native broadleaf species which grows well on soils of low fertility, no studies have been carried out to date to asses the basic productivity of the birch stands in this region, which is of primary concern in making forest management decisions.

To quantify site productivity, foresters usu-ally evaluate the relationship between tree height and age. A specific model of this relationship is known as site index (S, usually defined as the dominant height of a population of trees at a specific base or reference age). Although true site productivity may not be fully represented by site index, S is the most widely accepted method for estimating site productivity of even-aged forests. The development of dominant height over time may be modeled using self-referencing functions (Northway 1985), which use the current states of the considered phenomena (in this case height and age), to determine their past or predict their future states (Cieszewski 2003). For this purpose, so called “panel data” (i.e., pooled cross-sectional and time series data) are required. A pooled data-base blends characteristics of both cross-sectional and time series data. Like cross-sectional data, it describes each of a number of individuals. Like time series data, it describes each single individual over time. Pooled data are important because they contain the information necessary

to deal with both the inter-temporal dynamics and the individuality of the entities being investigated (Dielman 1989, p. 2). These sequences may be obtained by repeated measurements on permanent sample plots, or by stem analysis in which the past growth of trees is reconstructed from the annual growth ring patterns of the tree. The latter enables a faster way of constructing site curves. Its primary disadvantage is that height growth of individual trees may not represent height growth of stands, depending on the criterion used to select the trees.

The dominant height-age relationship can be modeled using either static or dynamic site equa-tions. Static equations have the following general form (omitting the vector of model parameters): Y = f(t,S), where Y is the value of the function (e.g., predicted height) at age t, and S is a fixed base-age (of say, 20 years) site index. Dynamic site equations have general form (again omitting the vector of model parameters): Y = f(t,t0,Y0), where Y is the value of the function at age t, and Y0 is the reference variable defined as the value of the function at age t0. Advantages of dynamic site equations have been pointed out in several studies (e.g., Cieszewski and Bailey 2000, Cieszewski 2001, 2003): they are base-age-invariant, and define both height-growth and site index models as special cases of the same equation, two desir-able properties in most applications. The invariant or unchanging property of dynamic equations refers to predicted heights: any number of points (t0,Y0) on a specific site curve can be used to make predictions for a given age t and the predicted height Y will always be the same. This is valid for both forward and backward predictions, which include the path invariance property, ensuring that the result of projecting first from t0 to t1, and then from t1 to t2, is the same as that of the one-step projection from t0 to t2. Bailey and Cieszewski (2000) noted that some models that have been referred to as ‘base-age invariant’ or ‘reference-age invariant’ do not have these comprehensive invariance properties.

Bailey and Clutter (1974) formalized the base-age invariance property and presented a tech-nique for dynamic equation derivation that is known in forestry as the Algebraic Difference Approach (ADA), and that essentially involves replacing a base-model’s site-specific parameter

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Diéguez-Aranda, Grandas-Arias, Álvarez-González and v. Gadow Site Quality Curves for Birch Stands in North-Western Spain

with its initial-condition solution. However, the concept of base-age-invariance of Bailey and Clutter (1974) also involves the estimation of the model parameters using a base-age-invariant (BAI) method (e.g., Bailey and Clutter 1974, García 1983, DuPlat and Tran-Ha 1986, 1997, Tait et al. 1988, Bégin and Schütz 1994, Gregoire et al. 1995, Gregoire and Schabenberger 1996, Cieszewski et al. 2000, Hall and Bailey 2001), which does not depend on the selection of any base age and which allows the use of all the data available. The main limitation of the ADA is that most models derived with it are either anamorphic or have single asymptotes (Bailey and Clutter 1974, Cieszewski and Bailey 2000).

Cieszewski and Bailey (2000) introduced a generalization of the ADA, the Generalized Alge-braic Difference Approach (GADA), which can be used to derive the same models derived by the ADA. The main advantage of the GADA is that the base equations can be expanded according to various theories about growth characteristics (e.g., asymptote, growth rate), allowing more than one parameter to be site-specific, and also the deri-vation of more flexible dynamic equations (see Cieszewski and Bailey 2000, Cieszewski 2001, 2002, 2003). This approach also includes simu-lation of concurrent polymorphism and multiple asymptotes, important properties of site equations (Cieszewski 2002).

The objective of the present study was to develop a base-age invariant equation, sensu Bailey and Clutter (1974), which realistically describes the dominant height growth of single-species, even-aged birch natural stands in Galicia.

2 Data

In the winter of 1998–1999 a network of 124 permanent plots was established in even-aged, birch-dominated stands (85% or more of the standing basal area consisting of birch – Betula pubescens). The plots were located throughout the area of distribution of this species in Galicia, and were subjectively selected to represent the existing range of ages, stand densities and sites. The plot size ranged from 200 m2 to 1000 m2, depending on stand density, in order to achieve

a minimum of 30 trees per plot. Although the smallest plot size (200 m2) may appear to be too small, only 2 plots were of this size. More than 50 trees were measured in these plots, which were selected to include extremes of site and stand conditions. The rest of the plots were larger than 500 m2. In the cases where was possible, two undamaged dominant trees were destructively sampled, constituting a final sample of 214 domi-nant trees. These trees were selected as the first two dominant trees found outside the plots but in the same stands within ±5% of the mean diameter at 1.3 m above ground level and mean height of the dominant trees (considered as the 100 larg-est-diameter trees per hectare). An exchange of trees over time within this group of dominant trees could be expected in forest stands (Eriksson et al. 1997). This so-called “rank effect” may be a problem when using height growth for single trees, reconstructed by means of stem analysis, for site curve modelling. Because of the strong light-demanding behaviour of birches the rank effect was assumed to be of minor importance and was thus neglected in this study. Therefore, according to Tennent and Burkhart (1981), the two trees selected in each stand following the above procedure were considered adequate for representing mean dominant height development of the stand with reasonable fidelity. The trees were felled leaving stumps of average height 0.20 m; total bole length was measured to the nearest 0.01 m. The logs were cut at approximately 1 m intervals until the diameter was 7 cm and at 2

Table 1. Total tree height statistics given in meters (age class 5 = 3–7 years, etc.).

Age No of Mean Standard Minimum Maximumclass obsevations deviation value value

5 800 2.50 1.40 0.3 6.910 611 5.97 1.81 1.1 11.815 401 8.77 2.30 2.0 15.620 273 11.33 2.53 3.6 17.825 116 13.33 2.53 7.5 20.930 44 13.74 3.77 8.3 24.435 52 16.62 2.33 8.9 20.940 27 17.35 1.46 14.2 19.845 15 18.71 2.53 14.8 22.950 3 18.17 2.64 16.4 21.255 5 18.14 1.19 16.8 19.865 1 15.85 15.9 15.9

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m intervals thereafter. The number of rings was counted at each cross sectioned point and then converted to stump age. As cross-section lengths did not coincide with periodic height growth, we adjusted the height/age data from stem analysis to account for this bias using Carmean’s method (Carmean 1972), and the modification proposed by Newberry (1991) for the topmost section of the tree, based on earlier studies (Dyer and Bailey 1987, Fabbio et al. 1994). The data were fur-ther examined for evidence of trees with past height growth abnormalities. Summary statistics including number of observations, mean, standard deviation, minimum, and maximum values of the selected data for modelling mean dominant height throughout the life of the stand were calculated for total tree height grouped by age classes (Table 1).

3 Methods

Cieszewski (2003) proposed a simplified approach of mixed-effects model for site curve develop-ment based on an adaptation of the nonlinear mixed-effects model defined by Lindstrom and Bates (1990). The method of Cieszewski (2003), which we employed in this study, is based on a one-point principle, i.e., it involves finding a model form that can describe the data well using only one parameter that varies with sites. If more than one parameter has to be site-specific, the varying parameters must be correlated with each other and defined as functions of fixed param-eters and just one common varying parameter. After such re-parameterization, all except the varying parameter are uncorrelated parameters of the base model, and can be defined as pure fixed effects. The varying parameter is the only parameter related to the random site effects (Lind-strom and Bates 1990), and can be defined as a mixed-effect parameter (including both fixed and random effects) comprising the sum of the global mean for this parameter (ψ) (plus a site-specific effect (ζi). The next steps involve: i) applying the GADA to ψ + ζi defined as X (a theoretical vector variable defining site quality with mean ψ and site-specific adjustments ζi with unknown distri-butional properties and an unknown magnitude

of values); ii) solving for X; iii) substituting the solution for X in the explicit three-dimensional site equation (Y = f(t,X)) for initial conditions t0 and Y0, (Y = f(t,t0,Y0)), where Y0 comprises for each individual the sum of the observed height at age t0 plus a site-specific effect e0i interpreted as the random measurement and environmental errors); and iv) fitting in one stage using a stochas-tic regression approach, or a varying parameter regression with desired fitting criteria. In the final fitting procedure, the estimated heights (i.e., Y0 at t0) obtained by fitting a curve to the observed trend in the data for each individual growth series are used as predictors rather than the observed heights. This direct use of data, as constants, does not violate regression assumptions because the measurement and environmental errors associated with these data are estimated simultaneously with all the other parameters of the model (Cieszewski 2003).

3.1 Models

A number of researchers have pointed out desira-ble attributes of site quality equations (Bailey and Clutter 1974, Gadow and Hui 1999, Cieszewski and Bailey 2000, Cieszewski 2002, 2003). The most frequently listed criteria are: polymorphism, sigmoid growth pattern with an inflexion point, horizontal asymptote at old ages, logical behavior (height should be zero at age zero and equal to site index at the reference age; the curve should never decrease), theoretical basis or interpretation of model parameters derived by analytically tracta-ble algebraic operations, base-age invariance, and path invariance. The fulfillment of these attributes depends on both the model construction method and the statistical procedures used for model fit-ting, and cannot always be achieved.

With these criteria in mind, we examined dif-ferent three-parameter base models and tested several variants for each one using the simplified approach of the mixed-effects model of Ciesze-wski (2003). Both one and two parameters were considered to be site-specific. Some of the models tested were discarded in the first steps because of their poor performance when applied to the data, especially those which considered only one site-specific parameter in the base model. Therefore,

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at least two parameters had to vary with site qual-ity to fit all data well, while one parameter was common for all sites without compromising the overall fit before it was used for derivation of the dynamic equations using the GADA. Equations that include two site-specific parameters allow simulation of concurrent polymorphism and mul-tiple asymptotes, which are both desirable char-acteristics of site equations (Cieszewski 2002). Although this may be of secondary importance, equations that possess these characteristics are more flexible, and better describe a wide variety of height-age trends.

We finally focused our efforts on two dynamic equations. Using general notational convention, a1, a2… an denoted parameters in base models, while b1, b2… bm were used for global param-eters in subsequent GADA formulations. All the GADA based models have the general implicit form Y = f(t,t0,Y0,b1,b2…bm).

The first model tested is based on the function proposed by Bertalanffy (1949, 1957) and studied by Richards (1959), whose integral form can, for simplicity, be represented as

Y = a1[1 – exp(–a2t)]a3 (1)

where a1 is an asymptote or limiting value, a2 is often called a ‘rate parameter’, and a3 is often referred to as ‘an initial pattern parameter’. This model is very flexible and has been frequently used for site-index curve development (e.g., Burk-hart and Tennent 1977, Powers and Oliver 1978, García 1983, Biging 1985, Diéguez-Aranda et al. 2005).

To derive a model with both polymorphism and variable asymptotes from Eq. 1, more than one parameter has to be a function of site pro-ductivity. When the parameter a2 varies with site productivity none of the other two parameters a1 or a3 can be used (together with a2) to model the site productivity response, because when a1 or a3 is a function of X and the parameter a2 is also a function of X, the model cannot be solved for X in a closed-form solution (Cieszewski 2004). Thus, in the model derived from Eq. 1 using the GADA, both the asymptote a1 and the shape parameter a3 are assumed to be dependent on X. To facilitate such derivation, the base equation is re-parameter-ized into a form more suitable for manipulation of

these two parameters (using exp(a1’) rather than a1 and taking the natural logarithm – ln – of the function) as follows:

Y = exp(a1’)[1 – exp(–a2t)]a3

ln(Y) = a1’ + a3 ln[1 – exp(–a2t)]

The site parameters can be conditioned to be consistently proportional to each other’s inverse over the site productivity dimension by the fol-lowing definitions:

a1’ = X and a3 = b2 + b3 / X, while a2 = b1

which defines the following GADA formula-tion:

ln(Y) = X + (b2 + b3 / X) ln[1 – exp(–b1t)] (2)

For this base equation, the solution for X involves finding roots of a quadratic equation and select-ing the most appropriate root for substituting into the dynamic equation. The selection of the most appropriate expression for X may depend on the equation parameters that in turn depend on the data and the domain of the applicable ages (Cieszewski and Bailey 2000). The solution for X in Eq. 2 with initial condition values t0 and Y0 is

X Y b L b L Y b L0 0 2 0 2 0 02

3 01

24= − ± − −

ln( ) ln( )

(3)

where L0 = ln[1 – exp(–b1t0)]

Selecting the root most likely to be real and positive (i.e., that involving addition rather than subtraction of the square root), and substituting it into Eq. 2 results in the following dynamic equation that provides polymorphic curves with variable asymptotes:

Y Yb t

b t

b b X

= − −− −

+

01

1 0

1

1

2 3 0exp( )

exp( )

/

(4)

The second model may be viewed as a special case of the log-logistic class of models, which are equivalent to Hossfeld models (Cieszewski 2000) and have a long history in describing a wide variety of population dynamics. Monserud (1984) applied a log-logistic model to describe the height growth of inland Douglas-fir in the northern Rocky Mountains (United States of

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America) based on data from stem analysis of dominant trees. Cieszewski (2000, 2001, 2002, 2003) examined several GADA formulations using the log-logistic model as a base equation, generally expressed as

Ya

a u=

+ −1

21 exp( ) (5)

where a1 is the asymptote parameter, a2 is the half-saturation parameter that defines the value of exp(–u) at which Y(u) = a1/2, and u is usu-ally a linear combination of log-transformations of the independent variable(s) (Monserud 1984, Cieszewski 2000, 2001, 2002).

Within the different forms investigated, the GADA based formulation by Cieszewski (2002) was found to perform particularly well. It consid-ers the above-mentioned u as a logarithmically transformed function of age, so that u = a3 lnt. Therefore, Eq. 5 can be simplified as the Hossfeld (1822) model

Ya

a t a=

+ −1

21 3 (6)

Cieszewski (2002) replaced a1 with a constant plus the unobserved site variable X, and a2 by b2 / X, while a3 = b3, becoming the GADA for-mulation

Yb X

b X t b= +

+ −1

21 3/ (7)

The solution for X involves again finding roots of a quadratic equation. With initial condition values t0 and Y0 it is

X Y b b Y b Y t b0 0 1 1 0

22 0 0

1

24 3= − ± − +

−( ) (8)

Selecting the root most likely to be real and positive (i.e., that involving addition rather than subtraction of the square root), and substituting it into Eq. 8 results in the following dynamic equation that provides polymorphic curves with variable asymptotes:

Yb X

b X t b= +

+ −1 0

2 01 3/ (9)

3.2 Analysis

Parameter estimation for site models involves many different statistical considerations such as fitting criteria, error structures and independence of errors, as well as homogeneity of variance. An effective system for parameter estimation must be based on identifying individual trends represented in the data (Bailey and Clutter 1974, García 1983, Cieszewski et al. 2000, Cieszewski 2003). These trends can be modeled by considering that indi-viduals’ responses all follow a similar functional form with parameters that vary among individu-als (local parameters) and parameters that are common for all individuals (global parameters). In practice both base-age specific (BAS) and base-age invariant (BAI) methods can be used. The assumption behind the BAS methods, which use selected data (i.e., site indices or heights at the given base age) as site-specific constants, is that the data measurements simultaneously do and do not contain measurement and environmental errors (on the left and right hand sides of the model respectively; e.g., Curtis et al. 1974, Mon-serud 1984), which is clearly untenable (Bailey and Clutter 1974). The assumption behind the BAI methods, which estimate site-specific effects, is that the data measurements always contain measurement and environmental errors (both on the left and right hand sides of the model) that must be modeled (Cieszewski 2003). In the BAI methods, the observation units – study plots or trees – of the panel data are assumed to constitute a random sample from a population of inter-est, while the observations within each unit are not random. Among the different BAI parameter estimation techniques reported in the introduc-tory section of the present study, we estimated the random site-specific effects simultaneously with the fixed effects using the dummy variables method described by Cieszewski et al. (2000). In this method the initial conditions are specified as identical for all the measurements belonging to the same tree. The initial age can be, within limits, arbitrarily selected, even for each tree; however, age zero is not allowed. The height corresponding to the initial age is then simultaneously estimated for each tree along with all of the global model parameters during the fitting process. Unlike the traditional technique (BAS method) that requires

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an arbitrary choice of base age prior to fitting the model, and then forcing the model through the chosen height/age point (site index), the BAI methods recognize that each measurement is made with error and, therefore, it seems unreasonable to force the model through any given measure-ment. Instead, the curve is fitted to the observed individual trends in the data. With this method all the data can be used, and there is no need to make any arbitrary choice regarding measurement intervals (i.e., it is a BAI method).

There are particular problems associated with the estimation of regression equations describing the behavior of individuals over time. In a single time series equation we often encounter the prob-lem of autocorrelated errors. In a single cross-sec-tional regression equation we may have problems with a non-constant error variance. When combin-ing cross-sectional and time series data we may find both problems occurring simultaneously. In addition, we may find that cross-sectional distur-bances for different individuals at the same point in time may be correlated. Such contemporaneous correlation is an added problem specific to the analysis of pooled data (Dielman 1989, p. 2).

In the general formulation of the nonlinear mixed-effects model used in this study the error terms eij are assumed to be independent and identically distributed with zero mean. Never-theless, because of the longitudinal nature of the data set used for model fitting, serial correlation (i.e., correlation between the residuals within the same individual) was expected. To account for this possible autocorrelation we modeled the error terms using a continuous-time autoregres-sive error structure (CAR(x)). This error structure permits the model to be applied to irregularly spaced, unbalanced data (Gregoire et al. 1995, Zimmerman and Núñez-Antón 2001), both of which are characteristics of many forestry data sets (West et al. 1984). The CAR(1) expands the error terms in the following way:

e d eijt t

ij ijij ij= +−

−−

1 1 11ρ e

where eij is the jth ordinary residual on the ith indi-vidual (i.e., the difference between the observed and the estimated heights of the tree i at age measurement j), d1 = 1 for j > 1 and it is zero for j = 1, ρ1 is the first-order autoregressive parameter

to be estimated, and tij – tij–1 is the time distance separating the jth from the jth–1 observations within each tree, tij > tij–1. In CAR(2) the error terms are expanded as:

e d e d eijt t

ijt t

ijij ij ij ij= + +−

−−

−− −

1 1 1 2 2 21 2ρ ρ e iij

consequently, d2 = 1 for j > 2 and it is zero for j ≤ 2, ρ2 is the second-order autoregressive parameter to be estimated, and tij – tij–2 is the time distance separating the jth from the jth–2 observations within each tree, tij > tij–2. The error terms for continuous-time autoregressive error structures of higher-order would be expanded similarly. In these cases eij are now independent and identi-cally distributed.

Visual examination of relevant graphs provided valuable insight into the statistical properties of the data. To evaluate the presence of autocor-relation and the order of the CAR(x) to be used, graphs representing residuals versus lag-residu-als from previous observations within each tree were examined visually. The dummy variables method including the CAR(x) error structure was programmed using the SAS/ETS® MODEL pro-cedure (SAS Institute Inc. 2004), which allows for dynamic updating of the residuals. To exam-ine if cross-sectional correlation existed, we first ordered the residuals in ascending order by age and height, and then examined visually the graphs of residuals versus lag-residuals from previous observations within the same age. The hetero-scedasticity was also examined visually, by plot-ting residuals as a function of predicted values, in order to investigate possible weighting factors.

3.3 Model Comparison

Comparison of the estimates for the two models tested was based on numerical and graphical analyses of the residuals. Two statistical criteria were examined: coefficient of determination for nonlinear regression (R2) and root mean square error (RMSE). Although there are several short-comings associated with use of the R2 in nonlin-ear regression, the general usefulness of some global measure of model adequacy would seem to override some of those limitations (Ryan 1997,

638


p. 424). The expressions of these statistics are summarized as follows:

R r

Y Y

n p

Y Y

i ii

n

i i

2 2

21

=

=−

−=∑

ˆ

( ˆ )RMSE

where rY Yi i is the correlation coefficient between

the measured (Yi) and estimated (Yi ) values of the dependent variable, n is the total number of observations used to fit the model, and p is the number of model parameters.

Because the quality of fit does not necessarily reflect the quality of future prediction (Myers 1990, p. 168), assessment of the validity of the model with an independent data set is recom-mended (Kozak and Kozak 2003). Due to the scarcity of such data, several methods have been proposed (e.g., splitting the data set or cross-validation, double cross-validation), although they seldom provide any additional information compared with the respective statistics obtained directly from models built from entire data sets (Kozak and Kozak 2003). Thus, because decisions have to be made with available information, it is better to wait to obtain new data before such validation is carried out.

The final step in evaluating the different fitted models involved visual examination of the residu-als against the estimated values, and the fitted curves for different site indices overlaid on the profile plots of the trees. Visual inspection is an essential point in selecting the most accurate model because curve profiles may differ consid-erably, even though goodness-of-fit statistics are similar.

3.4 Selection of Reference Age for Site Quality Evaluation

Practical use of the model to estimate site quality from any given pair of height and age requires the selection of a base age to which site index will be referenced. Inversely, site index and its associated base age may be used to estimate dominant height at any desired age. Therefore, the selection of a base age becomes an important issue when only one observation of a new individual is available.

The base age should be selected so that it is a reliable predictor of height at other ages, taking into account the erratic height growth at young ages but considering that a young base age will help in earlier decision making of the silvicultural treatments to be applied to the stand.

In order to address the first consideration, dif-ferent base ages and their corresponding observed heights were used to estimate heights at other ages (both forward and backward) for each tree. The results were compared with the values obtained from stem analysis, and the relative error in pre-dictions (RE%) was then calculated as follows:

RE% =− −

=∑ ( ˆ ) / ( )Y Y n p

Y

i ii

n 21 100

where Yi, Yi and Y are the observed, predicted and average values of the tree height, respec-tively; n is the total number of observations used to fit the model; and p is the number of model parameters.

It is important to note that the statistic described in this section is only meaningful if the site-specific parameters are discarded because of the lack of repeated data within the same individual. Otherwise, the site-specific parameters could be estimated and all the predictions would be identical whatever base age was selected (this is the essence of the BAI method used for model fitting).

4 Results and Discussion

We first fitted Eqs. 4 and 9 using nonlinear least squares without expanding the error terms to account for autocorrelation. A trend in residuals as a function of age-lag-residuals within the same tree was evident in both equations, as expected because of the longitudinal nature of the data used for model fitting. Fig. 1 (first row) provides an example with Eq. 9. After correction for auto-correlation using a second-order continuous-time autoregressive error structure, the trends in residu-als disappeared (Fig. 1 third row). The fitted curve shapes were not significantly altered as compared with the models fitted without considering such correction. It should be noted that, from a practi-

639


cal point of view, the autocorrelation parameters are generally ignored when using the site model for predicting height and site index. The quanti-ties eij–1 and eij–2 will probably not be known unless one is working with stem analysis data or with repeated measurements on the same plot, in which case they could be determined and used to

predict a future value. The main purpose in using the autocorrelation error structure was to obtain consistent estimates of the parameters and their standard errors.

The parameter estimates for each model and their corresponding goodness-of-fit statistics are shown in Table 2. All the parameters were signifi-

Table 2. Parameter estimates and goodness-of-fit statistics for the two models tested.

Model Parameter Estimate Approx. t-value Approx. RMSE R2

Std. Err. p-value

Eq. 4 b1 0.0480 0.0013 38.3 <0.0001 0.5043 0.9892 b2 –1.37 0.28 –4.9 <0.0001 b3 8.65 0.89 9.7 <0.0001 ρ1 0.940 0.012 79.4 <0.0001 ρ2 0.852 0.013 65.1 <0.0001Eq. 9 b1 19.8 1.7 11.7 <0.0001 0.5050 0.9892 b2 758 148 5.1 <0.0001 b3 1.40 0.02 80.9 <0.0001 ρ1 0.942 0.012 81.1 <0.0001 ρ2 0.847 0.013 63.7 <0.0001

Fig. 1. Residuals versus: Age-Lag1-Residuals (left column), Age-Lag2-Residuals (middle column), and Age-Lag3-Residuals (right column) for Eq. 9 fitted without considering the autocorrelation parameters (first row), and using continuous-time autoregressive error structures of first and second order (second and third rows, respectively).

Res

idua

ls(m

)

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

CA

R1

Res

idua

ls(m

)

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

CA

R2

Res

idua

ls(m

)

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

-3.0

-1.5

0.0

1.5

3.0

-3.0 -1.5 0.0 1.5 3.0

Age-Lag1-Residuals (m) Age-Lag2-Residuals (m) Age-Lag3-Residuals (m)

640


cant at the 1% level (including the site-specific parameters for each tree). The two models pro-vided similar results, as indicated by the graphs that represent the fitted curves for different site indices overlaid on the trajectories of the observed heights over time (Fig. 2) and the goodness-of-fit statistics (Table 2). These models explained 98.9% of the total variance, and provided a random pat-tern of residuals around zero with homogeneous variance and no detectable significant trends, both for predicted heights and site indices at a reference age of 20 years (Fig. 3 exemplified with Eq. 9). The use of two-site-specific parameter equations allowed simulation of concurrent polymorphism

and multiple asymptotes, a desirable characteris-tic of site equations (Cieszewski 2002).

Graphs showing bias and root mean square errors in the height estimates for different age classes were analyzed (Fig. 4). Both models behaved similarly for juvenile and intermedi-ate age classes, and also appeared to describe dominant height growth realistically for older age classes. Selection of the model was thus based on visual inspection of the graph of the fitted curves for different site indices overlaid on the trajecto-ries of the observed heights over time (Fig. 2). In this figure a slight underestimate of dominant height at old ages can be inferred for Eq. 4.

Fig. 2. Curves for site indices of 5, 9, 13 and 17 m at a reference age of 20 years overlaid on the trajectories of the observed heights over time for Eq. 4 (left) and Eq. 9 (right). The fittings were carried out using the dummy variables method and correcting autocorrelation with a second-order continuous-time autoregressive error structure.

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70Age (years)

He

igh

t (m

)

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70Age (years)

He

igh

t(m

)

-3.0

-1.5

0.0

1.5

3.0

0 5 10 15 20 25Predicted Heights (m)

Re

sid

ual

s(m

)

-3.0

-1.5

0.0

1.5

3.0

4 8 12 16 20Site Indices (m)

Re

sidu

als

(m)

Fig. 3. Residuals versus: predicted heights as an illustration of potential heteroscedasticity (left), and site indices at a reference age of 20 years (right) for Eq. 9 fitted with a second-order continuous-time autoregressive error structure.

641


Thus, from the two dynamic equations finally evaluated for height growth prediction and site classification of birch stands in Galicia, Eq. 9, i.e. the GADA formulation derived by Ciesze-wski (2002) from the Hossfeld (1822) model by considering parameters a1 and a2 as related to site productivity, was selected:

YX

X t= +

+ −19 8

1 7580

01 40

.

/ . (10)

where Y is the predicted height (meters) at age t (years),

X Y Y Y t0 0 0

20 0

1 401

219 8 19 8 4 758= − + −( ) + ×

−. . .

and Y0 and t0 represent the predictor height and age. Note that the site-specific parameters are dis-carded in a way similar to that used to discard the autocorrelation coefficients (Cieszewski 2001), because the general use of the model involves making predictions using observed height and its associated measurement age in new individuals.

As already mentioned, dynamic equations derived with the GADA allow direct prediction of heights at any age from any other reference height and age. Therefore, to estimate the domi-nant height (Y) of a stand for some desired age (t), given site index (S) and its associated base age (tS), substitute S for Y0 and tS for t0 in Eq. 10. Similarly, to estimate site index at some chosen base age, given stand height and age, substitute S for Y and tS for t in Eq. 10.

Eq. 10 fulfills most of the desirable properties that a site equation should possess, that is: it con-stitutes a parsimonious, three parameter, dynamic

site equation; it shows no trends in residuals; it predicts height equal to site index at base age and, although it is not defined for age zero *, the height limit when age tends to zero is zero; it is polymorphic with sigmoidal curve shapes and varying asymptotes; and it has the base-age and path invariance properties providing consistent predictions. Furthermore, it has been fitted using a base-age invariant method that accounts for site-specific and global effects, while addressing the error structure of the data.

The present investigation was based primarily on trees of height at 20 years of between 4 and 18 m. The ages ranged from 2 to more than 45 years. Therefore, the curves may be used over the entire rotation of the species in the region of study. The asymptotic values of the curves for site indices of 5, 9, 13 and 17 m at a reference age of 20 years resulted 23.0, 25.9, 29.1 and 32.4 m, respectively, which seems realistic. The curves should be used for ages greater than 5 years, since for younger ages erratic height growth may lead to erroneous classifications.

In selecting the base age, it was found that an age of 20 years was superior for predicting height at other ages (Fig. 5). Therefore, it is proposed as the base age to which site index will be refer-enced in order to classify the stands according to its productivity.

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

5 15 25 35 45 55Age class (years)

BIA

S(m

)

Eq. 4

Eq. 9

0.0

0.4

0.8

1.2

1.6

5 15 25 35 45 55Age class (years)

RM

SE

(m)

Eq. 4

Eq. 9

Fig. 4. Bias and root mean square error (RMSE) in height estimation for Eqs. 4 and 9 by 5-year age classes.

* This is not a fundamental property, albeit an identical model which is defined for t = 0 is the generalization of this equation in Cieszewski (2001) (Eq. 21 in the original publication).

642


Finally, the curves generated using Eq. 10 were compared with the curves developed by Eriksson et al. (1997) for pure and mixed stands of Betula pendula and Betula pubescens in Sweden, to evaluate possible similarities in dominant height growth of birch stands at contrasting latitudes in Europe. A wider spectrum of site qualities was found in Sweden than in Galicia (site indices of between 3.5 and 21 m at a reference age of 20 years compared with site indices of between 4 and 18 m at the same reference age, respectively). The better site qualities found in Sweden may be explained by the presence of some birch stands on abandoned farmland (Karlsson et al. 1998), i.e. on relatively fertile soils, whereas in Galicia the stands are mainly growing on relatively unfertile forest soils. Furthermore, the poorest site quali-ties found in Sweden may be explained by the shorter annual growth period in boreal areas. The two site quality systems for intermediate qualities were also compared (Fig. 6). The curves are quite similar until ages of 25–30 years, but culmination of growth is reached at younger ages in the Gali-cian stands. Nevertheless, these trends should be corroborated using the trajectories of the observed heights over time, used to develop the site quality curves for the two regions analyzed.

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0

10

20

30

40

0 10 20 30 40 50 60Age class (years)

RE

%

0

500

1000

1500

2000

2500

3000

No

. of ob

servatio

ns

RE%n

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70Age (years)

He

igh

t (m

)

Fig. 5. Relative error in height prediction (solid line) related to choice of reference age for Eq. 9 by 2-year age classes and number of observations n (dotted line).

Fig. 6. Comparison of the Galicia curves (solid lines) for site indices of 5, 9, 13 and 17 m at a reference age of 20 years for Eq. 9 against the curves developed by Eriksson et al. (1997; dotted lines)* for birch stands in Sweden, overlaid on the profile plots of the data set used in this study.

* Because the curves of Eriksson et al. (1997) were

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643


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Total of 47 references

site quality curves for birch stands in north-western spain

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